diff --git a/networks/lora.py b/networks/lora.py
index cd73cbe7..0c75cd42 100644
--- a/networks/lora.py
+++ b/networks/lora.py
@@ -241,9 +241,13 @@ class LoRAInfModule(LoRAModule):
         else:
             area = x.size()[1]
 
-        mask = self.network.mask_dic[area]
+        mask = self.network.mask_dic.get(area, None)
         if mask is None:
-            raise ValueError(f"mask is None for resolution {area}")
+            # raise ValueError(f"mask is None for resolution {area}")
+            # emb_layers in SDXL doesn't have mask
+            # print(f"mask is None for resolution {area}, {x.size()}")
+            mask_size = (1, x.size()[1]) if len(x.size()) == 2 else (1, *x.size()[1:-1], 1)
+            return torch.ones(mask_size, dtype=x.dtype, device=x.device) / self.network.num_sub_prompts
         if len(x.size()) != 4:
             mask = torch.reshape(mask, (1, -1, 1))
         return mask
@@ -348,9 +352,10 @@ class LoRAInfModule(LoRAModule):
             out[-self.network.batch_size :] = x[-self.network.batch_size :]  # real_uncond
 
         # print("to_out_forward", self.lora_name, self.network.sub_prompt_index, self.network.num_sub_prompts)
-        # for i in range(len(masks)):
-        #     if masks[i] is None:
-        #         masks[i] = torch.zeros_like(masks[-1])
+        # if num_sub_prompts > num of LoRAs, fill with zero
+        for i in range(len(masks)):
+            if masks[i] is None:
+                masks[i] = torch.zeros_like(masks[0])
 
         mask = torch.cat(masks)
         mask_sum = torch.sum(mask, dim=0) + 1e-4
diff --git a/sdxl_gen_img.py b/sdxl_gen_img.py
index 35bd6c61..c506ad3f 100755
--- a/sdxl_gen_img.py
+++ b/sdxl_gen_img.py
@@ -451,10 +451,11 @@ class PipelineLike:
         tes_text_embs = []
         tes_uncond_embs = []
         tes_real_uncond_embs = []
-        # use last pool
+
         for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
             token_replacer = self.get_token_replacer(tokenizer)
 
+            # use last text_pool, because it is from text encoder 2
             text_embeddings, text_pool, uncond_embeddings, uncond_pool, _ = get_weighted_text_embeddings(
                 tokenizer,
                 text_encoder,
@@ -529,6 +530,11 @@ class PipelineLike:
         c_vector = torch.cat([emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1)
         uc_vector = torch.cat([uc_emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1)
 
+        if reginonal_network:
+            # use last pool for conditioning
+            num_sub_prompts = len(text_pool) // batch_size
+            text_pool = text_pool[num_sub_prompts - 1 :: num_sub_prompts]  # last subprompt
+
         c_vector = torch.cat([text_pool, c_vector], dim=1)
         uc_vector = torch.cat([uncond_pool, uc_vector], dim=1)
 
@@ -762,7 +768,7 @@ class PipelineLike:
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
 
         if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            torch.cuda.empty_cache()
 
         if output_type == "pil":
             # image = self.numpy_to_pil(image)